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library(xgboost)Registered S3 method overwritten by 'data.table':
method from
print.data.table library(Matrix)
library(mclust) __ ___________ __ _____________
/ |/ / ____/ / / / / / ___/_ __/
/ /|_/ / / / / / / / /\__ \ / /
/ / / / /___/ /___/ /_/ /___/ // /
/_/ /_/\____/_____/\____//____//_/ version 5.4.9
Type 'citation("mclust")' for citing this R package in publications.ds0 <- readRDS("./ds0.rds")
ds1 <- readRDS("./ds1.rds")
ds2 <- readRDS("./ds2.rds")分发训练集
Idents(ds2) <- ds2$conditions
ds2_AC <- subset(ds2, idents = "AC")
ds2_PA <- subset(ds2, idents = "PA")
ds2_AC <- ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)Computing nearest neighbor graph
Computing SNNModularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3061
Number of edges: 99234
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9566
Number of communities: 4
Elapsed time: 0 secondsumapplot(ds2_AC)Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
Please use `as_label()` or `as_name()` instead.
This warning is displayed once per session.
ds2_PA <- ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)Computing nearest neighbor graph
Computing SNNModularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 6498
Number of edges: 215869
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9332
Number of communities: 3
Elapsed time: 0 secondsumapplot(ds2_PA)
AC_markers <- FindAllMarkers(ds2_AC,logfc.threshold = 0.7, min.diff.pct = 0.2)Calculating cluster 0
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s 在AC上训练
#混淆矩阵
predict_AC_test <- round(predict(bst_model, newdata = AC_test))
AC_confuse_matrix_test <- table(AC_test_data$label, predict_AC_test, dnn=c("true","pre"))
AC_confuse_matrix_test_prop <- prop.table(AC_confuse_matrix_test, 1)
AC_confuse_matrix_test_prop
pre
true 0 1 2 3
0 0.994652406 0.002673797 0.002673797 0.000000000
1 0.000000000 0.987755102 0.012244898 0.000000000
2 0.000000000 0.026455026 0.962962963 0.010582011
3 0.000000000 0.009009009 0.054054054 0.936936937在PA上训练
PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
set.seed(7)
index <- c(1:dim(PA_data)[2]) %>% sample(ceiling(0.3*dim(PA_data)[2]), replace = F, prob = NULL)
colnames(PA_data) <- NULL
PA_train_data <- list(data = t(as(PA_data[,-index],"dgCMatrix")), label = PA_label[-index])
PA_test_data <- list(data = t(as(PA_data[,index],"dgCMatrix")), label = PA_label[index])
# data(agaricus.train, pPAkage='xgboost')
PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)
PA_test <- xgb.DMatrix(data = PA_test_data$data,label = PA_test_data$label)
# xgb_params_train = {
# 'objective':'multi:softprob',
# 'eval_metric':'mlogloss',
# 'num_class':self.numbertrainclasses,
# 'eta':0.2,
# 'max_depth':6,
# 'subsample': 0.6}
# nround = 200
watchlist <- list(train = PA_train, eval = PA_test)
xgb_param <- list(eta = 0.2, max_depth = 6,
subsample = 0.6, num_class = length(table(Idents(ds2_PA))),
objective = "multi:softmax", eval_metric = 'mlogloss')
bst_model <- xgb.train(xgb_param, PA_train, nrounds = 200, watchlist, verbose = 0)
# 特征提取
importance <- xgb.importance(colnames(PA_train), model = bst_model)
head(importance)xgb.ggplot.importance(head(importance,20),n_clusters = 1)
multi_featureplot(head(importance,9)$Feature, ds2)
PA_genes <- head(importance, 500) ##选择top500
#混淆矩阵
predict_PA_test <- round(predict(bst_model, newdata = PA_test))
PA_confuse_matrix_test <- table(PA_test_data$label, predict_PA_test, dnn=c("true","pre"))
PA_confuse_matrix_test_prop <- prop.table(PA_confuse_matrix_test,1)
PA_confuse_matrix_test_prop pre
true 0 1 2
0 0.970982143 0.024553571 0.004464286
1 0.033603708 0.959443801 0.006952491
2 0.041884817 0.015706806 0.942408377adjustedRandIndex(PA_test_data$label, predict_PA_test) #PA分类器性能[1] 0.8821278#ROC曲线
# xgboost_roc <- pROC::multiclass.roc(PA_test_data$label, predict_PA_test) #多分类ROC
xgboost_roc <- pROC::roc(PA_test_data$label, predict_PA_test)Warning in roc.default(PA_test_data$label, predict_PA_test) :
'response' has more than two levels. Consider setting 'levels' explicitly or using 'multiclass.roc' instead
Setting levels: control = 0, case = 1
Setting direction: controls < casesplot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE,
grid=c(0.1, 0.2),grid.col=c("green", "red"),
max.auc.polygon=TRUE,auc.polygon.col="skyblue",
print.thres=TRUE,main='ROC curve') #前两个分量
选择特征common genes of top 500
使用所有来自PA的细胞训练分类器
应用在AC上，计算ARI
selected_features <- intersect(PA_genes$Feature, AC_genes$Feature)
PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_data <- PA_data[selected_features,]
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
colnames(PA_data) <- NULL
PA_train_data <- list(data = t(as(PA_data,"dgCMatrix")), label = PA_label)
PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)
xgb_param <- list(eta = 0.2, max_depth = 6,
subsample = 0.6, num_class = length(table(Idents(ds2_PA))),
objective = "multi:softmax", eval_metric = 'mlogloss')
bst_model <- xgb.train(xgb_param, PA_train, nrounds = 200, verbose = 1)
# 特征提取
importance <- xgb.importance(colnames(PA_train), model = bst_model)
head(importance)xgb.ggplot.importance(head(importance,20),n_clusters = 1)
multi_featureplot(head(importance,9)$Feature, ds2)
应用到AC上
AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_data <- AC_data[selected_features,]
AC_label <- as.numeric(as.character(Idents(ds2_AC)))
colnames(AC_data) <- NULL
AC_test_data <- list(data = t(as(AC_data,"dgCMatrix")), label = AC_label)
AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)
#计算混淆矩阵
predict_AC_test <- round(predict(bst_model, newdata = AC_test))
AC_confuse_matrix_test <- table(AC_test_data$label, predict_AC_test, dnn=c("true","pre"))
AC_confuse_matrix_test_prop <- prop.table(AC_confuse_matrix_test,1)
AC_confuse_matrix_test_prop #分析发育轨迹 pre
true 0 1 2
0 0.980360065 0.003273322 0.016366612
1 0.799516908 0.196859903 0.003623188
2 0.453004622 0.493066256 0.053929122
3 0.002762431 0.052486188 0.944751381#ROC曲线
# xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
xgboost_roc <- pROC::roc(AC_test_data$label, predict_AC_test)Warning in roc.default(AC_test_data$label, predict_AC_test) :
'response' has more than two levels. Consider setting 'levels' explicitly or using 'multiclass.roc' instead
Setting levels: control = 0, case = 1
Setting direction: controls < casesplot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE,
grid=c(0.1, 0.2),grid.col=c("green", "red"),
max.auc.polygon=TRUE,auc.polygon.col="skyblue",
print.thres=TRUE,main='ROC curve') #前两个分量ROC
# 计算ARI
adjustedRandIndex(predict_AC_test, AC_test_data$label)[1] 0.3024837选择特征common genes of top 500
使用所有来自AC的细胞训练分类器
应用在PA上，计算ARI
AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_data <- AC_data[selected_features,]
AC_label <- as.numeric(as.character(Idents(ds2_AC)))
colnames(AC_data) <- NULL
AC_train_data <- list(data = t(as(AC_data,"dgCMatrix")), label = AC_label)
AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)
xgb_ACram <- list(eta = 0.2, max_depth = 6,
subsample = 0.6, num_class = length(table(Idents(ds2_AC))),
objective = "multi:softmax", eval_metric = 'mlogloss')
bst_model2 <- xgb.train(xgb_ACram, AC_train, nrounds = 200, verbose = 1)
# 特征提取
importance2 <- xgb.importance(colnames(AC_train), model = bst_model2)
head(importance2)xgb.ggplot.importance(head(importance2,20),n_clusters = 1)
multi_featureplot(head(importance2,9)$Feature, ds2)
应用到PA上
PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_data <- PA_data[selected_features,]
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
colnames(PA_data) <- NULL
PA_test_data <- list(data = t(as(PA_data,"dgCMatrix")), label = PA_label)
PA_test <- xgb.DMatrix(data = PA_test_data$data,label = PA_test_data$label)
#计算混淆矩阵
predict_PA_test <- round(predict(bst_model2, newdata = PA_test))
PA_confuse_matrix_test <- table(PA_test_data$label, predict_PA_test, dnn=c("true","pre"))
PA_confuse_matrix_test_prop <- prop.table(PA_confuse_matrix_test,1)
PA_confuse_matrix_test_prop #分析发育轨迹 pre
true 0 1 2 3
0 0.027107438 0.287272727 0.682644628 0.002975207
1 0.000349895 0.075227432 0.914975507 0.009447166
2 0.008130081 0.003252033 0.175609756 0.813008130#ROC曲线
# xgboost_roc <- pROC::multiclass.roc(PA_test_data$label, predict_PA_test) #多分类ROC
xgboost_roc <- pROC::roc(PA_test_data$label, predict_PA_test)Warning in roc.default(PA_test_data$label, predict_PA_test) :
'response' has more than two levels. Consider setting 'levels' explicitly or using 'multiclass.roc' instead
Setting levels: control = 0, case = 1
Setting direction: controls < casesplot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE,
grid=c(0.1, 0.2),grid.col=c("green", "red"),
max.auc.polygon=TRUE,auc.polygon.col="skyblue",
print.thres=TRUE,main='ROC curve') #前两个分量ROC
# 计算ARI
adjustedRandIndex(predict_PA_test, PA_test_data$label)[1] 0.1797689umapplot(ds2,split.by = "conditions")
table(ds2$conditions)sankey plot
PA -> AC ARI 0.3024837
pretrue 0 1 2 0 0.980360065 0.003273322 0.016366612 1 0.799516908 0.196859903 0.003623188 2 0.453004622 0.493066256 0.053929122 3 0.002762431 0.052486188 0.944751381 ## AC ->PA ARI 0.1797689 pre true 0 1 2 3 0 0.027107438 0.287272727 0.682644628 0.002975207 1 0.000349895 0.075227432 0.914975507 0.009447166 2 0.008130081 0.003252033 0.175609756 0.813008130
数据集CA_dataset1
ds1 <- ds1 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.2)Computing nearest neighbor graph
Computing SNNModularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 4349
Number of edges: 142021
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8824
Number of communities: 5
Elapsed time: 0 secondsumapplot(ds1)
f("MYH11",ds1)
ds1_markers <- FindAllMarkers(ds1,logfc.threshold = 0.5, min.diff.pct = 0.2)Calculating cluster 0
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pre
true 0 1 2
0 1 1410 0
1 615 697 39
2 5 1119 3
3 1 65 333
4 44 4 13ds1_data_confuse_matrix_test_prop #分析发育轨迹 pre
true 0 1 2
0 0.0007087172 0.9992912828 0.0000000000
1 0.4552183568 0.5159141377 0.0288675056
2 0.0044365572 0.9929015084 0.0026619343
3 0.0025062657 0.1629072682 0.8345864662
4 0.7213114754 0.0655737705 0.2131147541冠状动脉数据集
ds0 <- ds0 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
Computing nearest neighbor graph
Computing SNNModularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5401
Number of edges: 173943
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9544
Number of communities: 5
Elapsed time: 0 secondsds0 <- ds0 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)Computing nearest neighbor graph
Computing SNNModularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5401
Number of edges: 173943
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9544
Number of communities: 5
Elapsed time: 0 secondsumapplot(ds0)
ds0_label <- as.numeric(as.character(Idents(ds0)))
colnames(ds0_data) <- NULL
ds0_test_data <- list(data = t(as(ds0_data,"dgCMatrix")), label = ds0_label)
ds0_test <- xgb.DMatrix(data = ds0_test_data$data,label = ds0_test_data$label)
#计算混淆矩阵
predict_ds0_test <- round(predict(bst_model, newdata = ds0_test))
ds0_data_confuse_matrix_test <- table(ds0_test_data$label, predict_ds0_test, dnn=c("true","pre"))
ds0_data_confuse_matrix_test_prop <- prop.table(ds0_data_confuse_matrix_test,1)
ds0_data_confuse_matrix_test
pre
true 0 1 2
0 2176 2 0
1 421 1360 4
2 981 232 0
3 173 0 0
4 52 0 0ds0_data_confuse_matrix_test_prop #分析发育轨迹 pre
true 0 1 2
0 0.9990817264 0.0009182736 0.0000000000
1 0.2358543417 0.7619047619 0.0022408964
2 0.8087386645 0.1912613355 0.0000000000
3 1.0000000000 0.0000000000 0.0000000000
4 1.0000000000 0.0000000000 0.0000000000#ROC曲线
# xgboost_roc <- pROC::multiclass.roc(ds0_test_data$label, predict_ds0_test) #多分类ROC
xgboost_roc <- pROC::roc(ds0_test_data$label, predict_ds0_test)Warning in roc.default(ds0_test_data$label, predict_ds0_test) :
'response' has more than two levels. Consider setting 'levels' explicitly or using 'multiclass.roc' instead
Setting levels: control = 0, case = 1
Setting direction: controls < casesplot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE,
grid=c(0.1, 0.2),grid.col=c("green", "red"),
max.auc.polygon=TRUE,auc.polygon.col="skyblue",
print.thres=TRUE,main='ROC curve') #前两个分量ROC
# 计算ARI
adjustedRandIndex(predict_ds0_test, ds0_test_data$label)[1] 0.2993046Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Ctrl+Alt+I.
When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Ctrl+Shift+K to preview the HTML file).
The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.
---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 

```{r}
library(xgboost)
library(Matrix)
library(mclust)
```

```{r}
ds0 <- readRDS("./ds0.rds")
ds1 <- readRDS("./ds1.rds")
ds2 <- readRDS("./ds2.rds")
```

# 分发训练集
```{r}
Idents(ds2) <- ds2$conditions
ds2_AC <- subset(ds2, idents = "AC")
ds2_PA <- subset(ds2, idents = "PA")
ds2_AC <- ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
umapplot(ds2_AC)
ds2_PA <- ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
umapplot(ds2_PA)

AC_markers <- FindAllMarkers(ds2_AC,logfc.threshold = 0.7, min.diff.pct = 0.2)
PA_markers <- FindAllMarkers(ds2_PA,logfc.threshold = 0.7, min.diff.pct = 0.2)
```

## 在AC上训练
```{r}
AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_label <- as.numeric(as.character(Idents(ds2_AC)))

set.seed(7)
index <- c(1:dim(AC_data)[2]) %>% sample(ceiling(0.3*dim(AC_data)[2]), replace = F, prob = NULL)

colnames(AC_data) <- NULL

AC_train_data <- list(data = t(as(AC_data[,-index],"dgCMatrix")), label = AC_label[-index])
AC_test_data <- list(data = t(as(AC_data[,index],"dgCMatrix")), label = AC_label[index])

# data(agaricus.train, package='xgboost')

AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)
AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)

# xgb_params_train = {
#     'objective':'multi:softprob',
#     'eval_metric':'mlogloss',
#     'num_class':self.numbertrainclasses,
#     'eta':0.2,
#     'max_depth':6,
#     'subsample': 0.6}
# nround = 200

watchlist <- list(train = AC_train, eval = AC_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_AC))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, AC_train, nrounds = 200, watchlist, verbose = 0)

# 特征提取
importance <- xgb.importance(colnames(AC_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1) #这个cluster和分类没有关系

multi_featureplot(head(importance,9)$Feature,ds2)
AC_genes <- head(importance, 500) ##选择top500


#混淆矩阵
predict_AC_test <- round(predict(bst_model, newdata = AC_test))

AC_confuse_matrix_test <- table(AC_test_data$label, predict_AC_test, dnn=c("true","pre"))
AC_confuse_matrix_test_prop <- prop.table(AC_confuse_matrix_test, 1)
AC_confuse_matrix_test_prop
#ROC曲线

# xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
xgboost_roc <- pROC::roc(AC_test_data$label, predict_AC_test)
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量

```


## 在PA上训练
```{r}
PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_label <- as.numeric(as.character(Idents(ds2_PA)))

set.seed(7)
index <- c(1:dim(PA_data)[2]) %>% sample(ceiling(0.3*dim(PA_data)[2]), replace = F, prob = NULL)

colnames(PA_data) <- NULL

PA_train_data <- list(data = t(as(PA_data[,-index],"dgCMatrix")), label = PA_label[-index])
PA_test_data <- list(data = t(as(PA_data[,index],"dgCMatrix")), label = PA_label[index])

# data(agaricus.train, pPAkage='xgboost')

PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)
PA_test <- xgb.DMatrix(data = PA_test_data$data,label = PA_test_data$label)

# xgb_params_train = {
#     'objective':'multi:softprob',
#     'eval_metric':'mlogloss',
#     'num_class':self.numbertrainclasses,
#     'eta':0.2,
#     'max_depth':6,
#     'subsample': 0.6}
# nround = 200

watchlist <- list(train = PA_train, eval = PA_test)
xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_PA))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, PA_train, nrounds = 200, watchlist, verbose = 0)

# 特征提取
importance <- xgb.importance(colnames(PA_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1)

multi_featureplot(head(importance,9)$Feature, ds2)
PA_genes <- head(importance, 500) ##选择top500


#混淆矩阵
predict_PA_test <- round(predict(bst_model, newdata = PA_test))

PA_confuse_matrix_test <- table(PA_test_data$label, predict_PA_test, dnn=c("true","pre"))
PA_confuse_matrix_test_prop <- prop.table(PA_confuse_matrix_test,1)
PA_confuse_matrix_test_prop

adjustedRandIndex(PA_test_data$label, predict_PA_test) #PA分类器性能

#ROC曲线

# xgboost_roc <- pROC::multiclass.roc(PA_test_data$label, predict_PA_test) #多分类ROC
xgboost_roc <- pROC::roc(PA_test_data$label, predict_PA_test)
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量

```
## 选择特征common genes of top 500
## 使用所有来自PA的细胞训练分类器
## 应用在AC上，计算ARI
```{r}
selected_features <- intersect(PA_genes$Feature, AC_genes$Feature)

PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_data <- PA_data[selected_features,]
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
colnames(PA_data) <- NULL

PA_train_data <- list(data = t(as(PA_data,"dgCMatrix")), label = PA_label)

PA_train <- xgb.DMatrix(data = PA_train_data$data,label = PA_train_data$label)

xgb_param <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_PA))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model <- xgb.train(xgb_param, PA_train, nrounds = 200, verbose = 1)

# 特征提取
importance <- xgb.importance(colnames(PA_train), model = bst_model)
head(importance)
xgb.ggplot.importance(head(importance,20),n_clusters = 1)

multi_featureplot(head(importance,9)$Feature, ds2)
```
# 应用到AC上
```{r}
AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_data <- AC_data[selected_features,]
AC_label <- as.numeric(as.character(Idents(ds2_AC)))
colnames(AC_data) <- NULL

AC_test_data <- list(data = t(as(AC_data,"dgCMatrix")), label = AC_label)

AC_test <- xgb.DMatrix(data = AC_test_data$data,label = AC_test_data$label)

#计算混淆矩阵
predict_AC_test <- round(predict(bst_model, newdata = AC_test))

AC_confuse_matrix_test <- table(AC_test_data$label, predict_AC_test, dnn=c("true","pre"))
AC_confuse_matrix_test_prop <- prop.table(AC_confuse_matrix_test,1)
AC_confuse_matrix_test_prop  #分析发育轨迹

#ROC曲线
# xgboost_roc <- pROC::multiclass.roc(AC_test_data$label, predict_AC_test) #多分类ROC
xgboost_roc <- pROC::roc(AC_test_data$label, predict_AC_test)
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量ROC

# 计算ARI 

adjustedRandIndex(predict_AC_test, AC_test_data$label)
```

## 选择特征common genes of top 500
## 使用所有来自AC的细胞训练分类器
## 应用在PA上，计算ARI
```{r}
AC_data <- get_data_table(ds2_AC, highvar = F, type = "data")
AC_data <- AC_data[selected_features,]
AC_label <- as.numeric(as.character(Idents(ds2_AC)))
colnames(AC_data) <- NULL

AC_train_data <- list(data = t(as(AC_data,"dgCMatrix")), label = AC_label)

AC_train <- xgb.DMatrix(data = AC_train_data$data,label = AC_train_data$label)

xgb_ACram <- list(eta = 0.2, max_depth = 6, 
                  subsample = 0.6,  num_class = length(table(Idents(ds2_AC))),
                  objective = "multi:softmax", eval_metric = 'mlogloss')

bst_model2 <- xgb.train(xgb_ACram, AC_train, nrounds = 200, verbose = 1)

# 特征提取
importance2 <- xgb.importance(colnames(AC_train), model = bst_model2)
head(importance2)
xgb.ggplot.importance(head(importance2,20),n_clusters = 1)

multi_featureplot(head(importance2,9)$Feature, ds2)
```


# 应用到PA上
```{r}
PA_data <- get_data_table(ds2_PA, highvar = F, type = "data")
PA_data <- PA_data[selected_features,]
PA_label <- as.numeric(as.character(Idents(ds2_PA)))
colnames(PA_data) <- NULL

PA_test_data <- list(data = t(as(PA_data,"dgCMatrix")), label = PA_label)

PA_test <- xgb.DMatrix(data = PA_test_data$data,label = PA_test_data$label)

#计算混淆矩阵
predict_PA_test <- round(predict(bst_model2, newdata = PA_test))

PA_confuse_matrix_test <- table(PA_test_data$label, predict_PA_test, dnn=c("true","pre"))
PA_confuse_matrix_test_prop <- prop.table(PA_confuse_matrix_test,1)
PA_confuse_matrix_test_prop  #分析发育轨迹

#ROC曲线
# xgboost_roc <- pROC::multiclass.roc(PA_test_data$label, predict_PA_test) #多分类ROC
xgboost_roc <- pROC::roc(PA_test_data$label, predict_PA_test)
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量ROC

# 计算ARI 

adjustedRandIndex(predict_PA_test, PA_test_data$label)
```



```{r}
umapplot(ds2,split.by = "conditions")
table(ds2$conditions)
```


# sankey plot

## PA -> AC     ARI 0.3024837
    pre
true           0           1           2
   0 0.980360065 0.003273322 0.016366612
   1 0.799516908 0.196859903 0.003623188
   2 0.453004622 0.493066256 0.053929122
   3 0.002762431 0.052486188 0.944751381
## AC ->PA    ARI 0.1797689
    pre
true           0           1           2           3
   0 0.027107438 0.287272727 0.682644628 0.002975207
   1 0.000349895 0.075227432 0.914975507 0.009447166
   2 0.008130081 0.003252033 0.175609756 0.813008130
```{r}
library(plotly)
c("PA_0", "PA_1", "PA_2", "AC_0","AC_1","AC_2","AC_3", "PA_0", "PA_1", "PA_2")
fig <- plot_ly(
    type = "sankey",
    orientation = "h",
    node = list(
      label = c("A1", "A2", "B1", "B2", "C1", "C2"),
      color = colors_list, 
      pad = 15,
      thickness = 20,
      line = list(
        color = "black",
        width = 0.5 )),
    link = list(
      source = c(0,1,0,2,3,3),
      target = c(2,3,3,4,4,5),
      value =  c(8,4,2,8,4,2)
    )
  )
fig <- fig %>% layout(
    title = "Basic Sankey Diagram",
    font = list(
      size = 10 ))

fig

```

---


# 数据集CA_dataset1
```{r}
ds1 <- ds1 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.2)
umapplot(ds1)
f("MYH11",ds1)
ds1_markers <- FindAllMarkers(ds1,logfc.threshold = 0.5, min.diff.pct = 0.2)
```

```{r}
ds1_data <- get_data_table(ds1, highvar = F, type = "data")
ds1_data <- ds1_data[selected_features,]
ds1_label <- as.numeric(as.character(Idents(ds1)))
colnames(ds1_data) <- NULL

ds1_test_data <- list(data = t(as(ds1_data,"dgCMatrix")), label = ds1_label)

ds1_test <- xgb.DMatrix(data = ds1_test_data$data,label = ds1_test_data$label)

#计算混淆矩阵
predict_ds1_test <- round(predict(bst_model, newdata = ds1_test))

ds1_data_confuse_matrix_test <- table(ds1_test_data$label, predict_ds1_test, dnn=c("true","pre"))
ds1_data_confuse_matrix_test_prop <- prop.table(ds1_data_confuse_matrix_test,1)
ds1_data_confuse_matrix_test
ds1_data_confuse_matrix_test_prop  #分析发育轨迹



#ROC曲线
# xgboost_roc <- pROC::multiclass.roc(ds1_test_data$label, predict_ds1_test) #多分类ROC
xgboost_roc <- pROC::roc(ds1_test_data$label, predict_ds1_test)
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量ROC

# 计算ARI 

adjustedRandIndex(predict_ds1_test, ds1_test_data$label)
```
# 冠状动脉数据集


```{r}
ds0 <- ds0 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
umapplot(ds0)
f("MYH11",ds0)
ds0_markers <- FindAllMarkers(ds0,logfc.threshold = 0.5, min.diff.pct = 0.2)
```

```{r}
temp <- get_data_table(ds0, highvar = F, type = "data")
ds0_data <- matrix(data=0, nrow = length(selected_features), ncol = length(colnames(temp)), byrow = FALSE, dimnames = list(selected_features,colnames(temp)))


for(i in intersect(selected_features,rownames(temp))){
  ds0_data[i,] <- temp[i,]
}
# rm(temp)

ds0_label <- as.numeric(as.character(Idents(ds0)))
colnames(ds0_data) <- NULL

ds0_test_data <- list(data = t(as(ds0_data,"dgCMatrix")), label = ds0_label)

ds0_test <- xgb.DMatrix(data = ds0_test_data$data,label = ds0_test_data$label)

#计算混淆矩阵
predict_ds0_test <- round(predict(bst_model, newdata = ds0_test))

ds0_data_confuse_matrix_test <- table(ds0_test_data$label, predict_ds0_test, dnn=c("true","pre"))
ds0_data_confuse_matrix_test_prop <- prop.table(ds0_data_confuse_matrix_test,1)
ds0_data_confuse_matrix_test
ds0_data_confuse_matrix_test_prop  #分析发育轨迹



#ROC曲线
# xgboost_roc <- pROC::multiclass.roc(ds0_test_data$label, predict_ds0_test) #多分类ROC
xgboost_roc <- pROC::roc(ds0_test_data$label, predict_ds0_test)
plot(xgboost_roc, print.auc=TRUE, auc.polygon=TRUE, 
  grid=c(0.1, 0.2),grid.col=c("green", "red"), 
  max.auc.polygon=TRUE,auc.polygon.col="skyblue", 
  print.thres=TRUE,main='ROC curve') #前两个分量ROC

# 计算ARI 

adjustedRandIndex(predict_ds0_test, ds0_test_data$label)
```




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